Abstract
The study presented in this article aims at understanding the relevance of mobility initiatives to the internationalisation efforts of Higher Education Institutions (HEIs). By building upon relevant literature, 17 propositions related to this contribution were identified. Empirical evidence from a concrete case of a European university was then used to evaluate those propositions. Data was collected from individual interviews to 19 outgoing faculty and from focus groups with 32 incoming students, resulting in the identification of the promises and pitfalls of mobility. The study concludes that HEIs must define clear strategies and carefully manage their mobility activities to maximise the potential benefits for internationalisation. Based on this main implication, it presents a set of managerial recommendations that may be relevant for those involved in administering or promoting international mobility programmes at universities, governments or international organisations, and for researchers in higher education.

Abstract
Recent changes in the energy sector are increasing the importance of portfolio optimization for market participation. Although the portfolio optimization problem is most popular in finance and economics, it is only recently being subject of study and application in electricity markets. Risk modeling in this domain is, however, being addressed as in the classic portfolio optimization problem, where investment diversity is the adopted measure to mitigate risk. The increasing unpredictability of market prices as reflection of the renewable generation variability brings a new dimension to risk formulation, as market participation risk should consider the prices variation in each market. This paper thereby proposes a new portfolio optimization model, considering a new approach for risk management. The problem of electricity allocation between different markets is formulated as a classic portfolio optimization problem considering market prices forecast error as part of the risk asset. Dealing with a multi-objective problem leads to a heavy computational burden, and for this reason a particle swarm optimization-based method is applied. A case study based on real data from the Iberian electricity market demonstrates the advantages of the proposed approach to increase market players? profits while minimizing the market participation risk.

Abstract

Abstract
The energy system is undergoing a drastic transition towards a system where previously passive consumers will play important roles. These consumers who actively participate in the energy system with a variety of distributed energy resources, such as electric vehicles, solar panels, and battery energy storage systems, become so-called prosumers as they can also generate electricity. This electricity can then be self-consumed, sold to the existing grid, or be sold to other consumers connected to the same electric network through Peer-to-Peer (P2P) trading schemes. This P2P energy trading may offer significant advantages to consumers involved as well as the wider electric system. The use of Agent-Based Modelling (ABM) can help address these problems. ABM models allow to understand complex and dynamic systems by incorporating the behavior of individual agents into the model as the individual behavior of the agents has a direct influence on the outcomes of the systems. In this paper, an ABM model is developed to examine the effects of increased consumer participation within a local energy system. This model utilizes a diverse set of consumers based on real-world data to model and provide insight into the interactions within a P2P energy trading system. The effects of P2P trading on financial outcomes as well as the share of renewable energy utilized within the local energy system is investigated. Results show that ABM models can accurately model P2P energy trading systems and can capture the effects of individual behavior of many active consumers within electrical systems. Also, it is shown that there may be a tradeoff between maximizing P2P energy trades within a community and maximizing the revenues of the prosumers.

Abstract
In recent years, there has been a growing need for measuring and understand how to foster Trust in technology. This need for understanding the trust factor, which potential transformed (in a short time) the way we work, learn and teach. It allowed us to realize that a simple technological tool per si can be a means to facilitate a task or an objective but not necessarily is a solution to create sustainable interactions. This sustainability comes together with the insurance that irrespective of the ability to monitor or control, we are willing to be vulnerable to another party’s actions based on the expectation that the other will perform a particular action important to us. We define Trust, as an attitude, an intention or behaviour. We see Trust as an interpersonal phenomenon that promotes social activities such as collaboration, sharing, or social capital creation. But also as a factor that facilitates interaction and participation in remote and networked contexts. Trust is an instrument that supports and regulates technological mediation processes, encourages technology interactions and continuous adoption. In this study’s scope, we seek to illustrate the state of the art of the different methodologies of analysis, design and reliable assessment of interactive systems. We were briefly contextualizing the problem and defining Trust from a Human-Computer Interaction point of view. Conclude with a reflection on how and how these practices can be pressing to develop more sustainable online mediation tools, which also encourage the participation of the actors involved. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

Abstract
Residential customers account for an indispensable part in the demand response (DR) program for their capability to provide flexibility when the system required. However, their available DR capacity has not been fully comprehended by the aggregator, who needs the information to bid accurately on behalf of the residential customers in the market transaction. To this end, this article devised an optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers. First, we attempt to establish the customers' responsiveness function in relation to different incentives, during which a home energy management system is introduced to implement load adjustment for electrical appliances. Second, the functional relation is applied to the aggregator's decision-making process to formulate the optimal bidding strategy in the day-ahead market and the optimal scheduling scheme for the energy storage system with the aim to maximize its own revenue. Finally, the validity of the proposed method is verified using the dataset from the Pecan Street experiment in Austin. The obtained outcome demonstrates the practical rationality of the proposed method.